Characterizing the effects of SiC and Al2O3 on the mechanical properties of Al6082 hybrid metal matrix composites: An experimental and neural network approach

Abstract

The use of advanced materials in the field of aerospace and automotive applications has led to use of metal matrix composites (MMC’s) due to their excellent mechanical properties. Aluminium metal matrix composite is one of the materials which can be strengthened by reinforcing it with hard ceramic particles. In the current work Al6082 matrix hybrid composites reinforced with silicon carbide (SiC) and aluminium oxide (Al2O3) was developed by using stir casting technique. The weight percentage of SiC was varied from 0 wt.% to 8 wt.% and keeping 3 wt.% Al2O3 constants. The tensile, hardness, density and impact tests were conducted, and the results obtained revealed that the addition of silicon carbide and Al2O3 particles in Al6082 enhances the mechanical properties of the prepared hybrid composites. The artificial neural network (ANN) model, which was trained using a dataset consisting of experimental results, has effectively captured the correlation between the weight percentage (wt.%) of silicon carbide (SiC) and the mechanical properties of the composite material. Through the examination of this model, valuable insights can be obtained regarding the distinct contributions of SiC to the mechanical properties of Al6082.